Bioscience Methods 2024, Vol.15, No.6, 264-274 http://bioscipublisher.com/index.php/bm 265 This study will analyze the advancements in genetic improvement techniques for enhancing sweet potato yield and starch content, delving into recent achievements in genetic engineering, genome editing, and traditional breeding methods. It aims to provide a comprehensive overview of the current state of research while identifying potential future directions. The ultimate goal is to highlight effective strategies for increasing sweet potato yield and starch content, thereby contributing to food security and sustainable agriculture. 2 Genetic Basis of Sweet Potato Yield and Starch Content 2.1 Genetic background of yield traits in sweet potato The yield traits in sweet potato are influenced by multiple quantitative trait loci (QTL) and genetic factors. For instance, a study identified two major QTL on linkage groups 3 and 12 that affect starch content, β-carotene, dry matter, and flesh color. These QTL regions act pleiotropically, reducing starch content while increasing β-carotene in genotypes carrying specific haplotypes (Gemenet et al., 2019). Another study using a polyploid genome-wide association study (GWAS) identified significant SNPs associated with starch content, dry matter, and storage root fresh weight, highlighting the complex genetic architecture of these traits (Haque et al., 2023). Additionally, QTL mapping in potato, a close relative of sweet potato, has revealed that QTLs for tuber yield and starch content are often linked, suggesting shared genetic control. 2.2 Starch synthesis pathways and related genes Starch synthesis in sweet potato involves several key biochemical pathways and genes. The primary enzymes include ADP-glucose pyrophosphorylase (AGPase), soluble starch synthase (SSS), and starch branching enzyme (SBE). AGPase catalyzes the first step in starch biosynthesis, converting glucose-1-phosphate and ATP to ADP-glucose, which is then used by SSS to elongate the starch chain. SBE introduces branch points into the starch molecule, creating a more complex structure (Menéndez et al., 2002). In sweet potato, genes such as granule-bound starch synthase I (IbGBSSI) have been identified as crucial for amylose biosynthesis, with consistent expression during starch accumulation (Haque et al., 2023). Additionally, the physical linkage of phytoene synthase with sucrose synthase has been shown to negatively correlate β-carotene and starch content, indicating a complex interplay between these pathways (Gemenet et al., 2019) 2.3 Gene expression and environmental interactions Environmental factors such as soil quality and climate significantly influence gene expression, impacting yield and starch content in sweet potato. High heritability and genetic advance for traits like vine length, number of branches, and root yield per plant suggest that these traits are less influenced by environmental factors and are governed by additive genes (Kar et al., 2022). However, the expression of genes involved in starch metabolism can be modulated by environmental conditions. For example, cold storage conditions in potato tubers lead to the accumulation of reducing sugars due to the activity of genes like invertase, which are also relevant in sweet potato (Li et al., 2005). Furthermore, QTLs for traits like cold-induced sweetening and reconditioning in potato have been mapped to specific chromosomes, indicating that environmental interactions can have a significant genetic basis (Xiao et al., 2018). 3 Role of Traditional Breeding in Sweet Potato Improvement 3.1 Phenotypic selection and hybrid breeding Traditional breeding methods, such as phenotypic selection and hybrid breeding, have been instrumental in improving sweet potato yield and starch content. Phenotypic selection involves choosing plants with desirable traits based on observable characteristics. This method has been effective in identifying high-yield and high-starch varieties, as demonstrated by the significant genetic variability and potential for genetic gains in sweet potato populations (Otoboni et al., 2020; Vargas et al., 2020). For instance, the study by Otoboni et al. (2020) showed that 81.25% of the traits had genotypic coefficients of variation above 20%, indicating favorable conditions for selection with considerable genetic advances. However, phenotypic selection has its limitations. The process is labor-intensive and time-consuming, requiring multiple generations to achieve significant improvements. Additionally, the selection is often influenced by
RkJQdWJsaXNoZXIy MjQ4ODYzNA==